Integration of Deep Reinforcement Learning and Cloud Computing for Enhanced Sports Performance Prediction and Training Guidance

Abstract

Improving sports performance with smart technology is becoming more important, especially in highly competitive fields like swimming. Many current training methods do not provide real-time feedback, lack flexibility, and fail to meet the unique needs of each swimmer. To solve these problems, the SwimInsight Reinforcement Learning Training Framework SIRLTF is introduced. SIRLTF integrates Long Short Term Memory LSTM for temporal modeling and Deep Reinforcement Learning algorithms including Deep Q Network DQN and Proximal Policy Optimization PPO for personalized feedback generation in a cloud- based environment. The cloud infrastructure enables fast data processing, large storage capacity, and remote accessibility, making the system efficient and scalable. SIRLTF is designed for use in professional swimming centers, sports academies, and athlete rehabilitation programs. It helps coaches and swimmers track progress, adjust training plans dynamically, and improve overall performance with intelligent data- driven suggestions. The framework also adapts continuously to changes in a swimmer's condition and training response. Comparative experiments as described in Section 4 show a 19 point 4 percent improvement in prediction accuracy and a 21 point 7 percent gain in training efficiency over baseline models such as CNN SVM and EC DRLMS. These results demonstrate the benefits of combining deep learning with cloud computing to enhance sports training. Overall, SIRLTF offers a smart, reliable, and scalable solution to improve swimming performance and can be extended to other sports for better athlete development.